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Besides assuring global optimality to a significant extent AHGA is also found to be an efficient algorithm requiring only one tuning error parameter saving considerable time on the part of the user.
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The proposed algorithm is found to be computationally efficient and is suitable for real-time design of an ANN.
On the contrary, greedy algorithms are found to be relatively efficient while uncovering functional modules from large biological networks.
Although less popular, several recent TD algorithms have been found to be highly efficient, especially in document classification problems.
The algorithm using RDW value of >15% with Hb ≤10 g/dL was found to be more efficient.
This optimization algorithm is then found to be numerically very efficient.
The enhanced Model Predictive Control (eMPC) algorithm has been studied in several clinical trials where it was found to be efficient and safe [ 22- 27].
Also, the simplified algorithm is found to be much more computationally efficient than the iterative technique.
Test problems, are used to validate this algorithm which is found to be more accurate and efficient than previous ones.
These algorithms though computationally not efficient, found to be effective when there is high similarity between patterns of dataset.
The applied semiautomatic algorithm was found to be time- and interaction-efficient and allowed performing various three-dimensional measurements on the resulting model.
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